from typing import Callable, Dict, Optional, Tuple

import torch
from torch import nn
from torch.distributions import (
    AffineTransform,
    Distribution,
    Independent,
    NegativeBinomial,
    Normal,
    StudentT,
    TransformedDistribution,
)
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):







   def __init__(     self						:						str   ,   lowerCamelCase_						:						Distribution   ,   lowerCamelCase_						:						List[str]=None   ,   lowerCamelCase_						:						Tuple=None   ,   lowerCamelCase_						:						str=0				):





    """simple docstring"""


    UpperCamelCase			  =      1.0 if scale is None else scale
    UpperCamelCase			  =      0.0 if loc is None else loc

    super().__init__(lowerCamelCase_   ,   [AffineTransform(loc=self.loc   ,   scale=self.scale   ,   event_dim=lowerCamelCase_				)]				)







   @property
   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    return self.base_dist.mean * self.scale + self.loc







   @property
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    return self.base_dist.variance * self.scale**2







   @property
   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    return self.variance.sqrt()





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Tuple   ,   lowerCamelCase_						:						int   ,   lowerCamelCase_						:						Dict[str, int]   ,   lowerCamelCase_						:						Callable[..., Tuple[torch.Tensor]]   ,   **lowerCamelCase_						:						Optional[int]				):





    """simple docstring"""


    super().__init__(**lowerCamelCase_				)
    UpperCamelCase			  =      args_dim
    UpperCamelCase			  =      nn.ModuleList([nn.Linear(lowerCamelCase_   ,   lowerCamelCase_				) for dim in args_dim.values()]				)
    UpperCamelCase			  =      domain_map







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						torch.Tensor				):





    """simple docstring"""


    UpperCamelCase			  =      [proj(lowerCamelCase_				) for proj in self.proj]

    return self.domain_map(*lowerCamelCase_				)





class SCREAMING_SNAKE_CASE_		(      nn.Module		):







   def __init__(     self						:						Any   ,   lowerCamelCase_						:						int				):





    """simple docstring"""


    super().__init__()
    UpperCamelCase			  =      function







   def 						lowerCamelCase_	(     self						:						Optional[int]   ,   lowerCamelCase_						:						Dict   ,   *lowerCamelCase_						:						Optional[Any]				):





    """simple docstring"""


    return self.function(lowerCamelCase_   ,   *lowerCamelCase_				)





class SCREAMING_SNAKE_CASE_		:
   __lowerCAmelCase           =				42
   __lowerCAmelCase           =				42
   __lowerCAmelCase           =				42







   def __init__(     self						:						str   ,   lowerCamelCase_						:						int = 1				):





    """simple docstring"""


    UpperCamelCase			  =      dim
    UpperCamelCase			  =      {k: dim * self.args_dim[k] for k in self.args_dim}







   def 						lowerCamelCase_	(     self						:						str   ,   lowerCamelCase_						:						Any				):





    """simple docstring"""


    if self.dim == 1:
     return self.distribution_class(*lowerCamelCase_				)
    else:
     return Independent(self.distribution_class(*lowerCamelCase_				)   ,   1				)







   def 						lowerCamelCase_	(     self						:						int   ,   lowerCamelCase_						:						Dict   ,   lowerCamelCase_						:						Optional[torch.Tensor] = None   ,   lowerCamelCase_						:						Optional[torch.Tensor] = None   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      self._base_distribution(lowerCamelCase_				)
    if loc is None and scale is None:
     return distr
    else:
     return AffineTransformed(lowerCamelCase_   ,   loc=lowerCamelCase_   ,   scale=lowerCamelCase_   ,   event_dim=self.event_dim				)







   @property
   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    return () if self.dim == 1 else (self.dim,)







   @property
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    return len(self.event_shape				)







   @property
   def 						lowerCamelCase_	(     self						:						str				):





    """simple docstring"""


    return 0.0







   def 						lowerCamelCase_	(     self						:						Optional[int]   ,   lowerCamelCase_						:						int				):





    """simple docstring"""


    return ParameterProjection(
        in_features=lowerCamelCase_   ,   args_dim=self.args_dim   ,   domain_map=LambdaLayer(self.domain_map				)   ,   )







   def 						lowerCamelCase_	(     self						:						Optional[int]   ,   *lowerCamelCase_						:						torch.Tensor				):





    """simple docstring"""


    raise NotImplementedError()







   @staticmethod
   def 						lowerCamelCase_	(     lowerCamelCase_						:						torch.Tensor				):





    """simple docstring"""


    return (x + torch.sqrt(torch.square(lowerCamelCase_				) + 4.0				)) / 2.0





class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				{"df": 1, "loc": 1, "scale": 1}
   __lowerCAmelCase           =				StudentT







   @classmethod
   def 						lowerCamelCase_	(     cls						:						Optional[Any]   ,   lowerCamelCase_						:						torch.Tensor   ,   lowerCamelCase_						:						torch.Tensor   ,   lowerCamelCase_						:						torch.Tensor				):





    """simple docstring"""


    UpperCamelCase			  =      cls.squareplus(lowerCamelCase_				).clamp_min(torch.finfo(scale.dtype				).eps				)
    UpperCamelCase			  =      2.0 + cls.squareplus(lowerCamelCase_				)
    return df.squeeze(-1				), loc.squeeze(-1				), scale.squeeze(-1				)





class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				{"loc": 1, "scale": 1}
   __lowerCAmelCase           =				Normal







   @classmethod
   def 						lowerCamelCase_	(     cls						:						List[str]   ,   lowerCamelCase_						:						torch.Tensor   ,   lowerCamelCase_						:						torch.Tensor				):





    """simple docstring"""


    UpperCamelCase			  =      cls.squareplus(lowerCamelCase_				).clamp_min(torch.finfo(scale.dtype				).eps				)
    return loc.squeeze(-1				), scale.squeeze(-1				)





class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				{"total_count": 1, "logits": 1}
   __lowerCAmelCase           =				NegativeBinomial







   @classmethod
   def 						lowerCamelCase_	(     cls						:						Tuple   ,   lowerCamelCase_						:						torch.Tensor   ,   lowerCamelCase_						:						torch.Tensor				):





    """simple docstring"""


    UpperCamelCase			  =      cls.squareplus(lowerCamelCase_				)
    return total_count.squeeze(-1				), logits.squeeze(-1				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]   ,   lowerCamelCase_						:						List[str]				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      distr_args
    if self.dim == 1:
     return self.distribution_class(total_count=lowerCamelCase_   ,   logits=lowerCamelCase_				)
    else:
     return Independent(self.distribution_class(total_count=lowerCamelCase_   ,   logits=lowerCamelCase_				)   ,   1				)







   def 						lowerCamelCase_	(     self						:						Optional[Any]   ,   lowerCamelCase_						:						Any   ,   lowerCamelCase_						:						Optional[torch.Tensor] = None   ,   lowerCamelCase_						:						Optional[torch.Tensor] = None				):





    """simple docstring"""


    UpperCamelCase  ,     UpperCamelCase			  =      distr_args

    if scale is not None:
     # See scaling property of Gamma.
     logits += scale.log()

    return self._base_distribution((total_count, logits)				)

import unittest

from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class SCREAMING_SNAKE_CASE_		(      unittest.TestCase		):
   __lowerCAmelCase           =				JukeboxTokenizer
   __lowerCAmelCase           =				{
       """artist""": """Zac Brown Band""",
       """genres""": """Country""",
       """lyrics""": """I met a traveller from an antique land,
        Who said \"Two vast and trunkless legs of stone
        Stand in the desert. . . . Near them, on the sand,
        Half sunk a shattered visage lies, whose frown,
        And wrinkled lip, and sneer of cold command,
        Tell that its sculptor well those passions read
        Which yet survive, stamped on these lifeless things,
        The hand that mocked them, and the heart that fed;
        And on the pedestal, these words appear:
        My name is Ozymandias, King of Kings;
        Look on my Works, ye Mighty, and despair!
        Nothing beside remains. Round the decay
        Of that colossal Wreck, boundless and bare
        The lone and level sands stretch far away
        """,
   }







   @require_torch
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    import torch

    UpperCamelCase			  =      JukeboxTokenizer.from_pretrained("""openai/jukebox-1b-lyrics"""				)
    UpperCamelCase			  =      tokenizer(**self.metas				)["""input_ids"""]
    # fmt: off
    UpperCamelCase			  =      [
        torch.tensor([[
            0, 0, 0, 7169, 507, 9, 76, 39, 31, 46, 76, 27,
            76, 46, 44, 27, 48, 31, 38, 38, 31, 44, 76, 32,
            44, 41, 39, 76, 27, 40, 76, 27, 40, 46, 35, 43,
            47, 31, 76, 38, 27, 40, 30, 64, 78, 76, 76, 76,
            76, 76, 76, 76, 76, 23, 34, 41, 76, 45, 27, 35,
            30, 76, 71, 20, 49, 41, 76, 48, 27, 45, 46, 76,
            27, 40, 30, 76, 46, 44, 47, 40, 37, 38, 31, 45,
            45, 76, 38, 31, 33, 45, 76, 41, 32, 76, 45, 46,
            41, 40, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
            19, 46, 27, 40, 30, 76, 35, 40, 76, 46, 34, 31,
            76, 30, 31, 45, 31, 44, 46, 63, 76, 63, 76, 63,
            76, 63, 76, 14, 31, 27, 44, 76, 46, 34, 31, 39,
            64, 76, 41, 40, 76, 46, 34, 31, 76, 45, 27, 40,
            30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76, 8,
            27, 38, 32, 76, 45, 47, 40, 37, 76, 27, 76, 45,
            34, 27, 46, 46, 31, 44, 31, 30, 76, 48, 35, 45,
            27, 33, 31, 76, 38, 35, 31, 45, 64, 76, 49, 34,
            41, 45, 31, 76, 32, 44, 41, 49, 40, 64, 78, 76,
            76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76, 49,
            44, 35, 40, 37, 38, 31, 30, 76, 38, 35, 42, 64,
            76, 27, 40, 30, 76, 45, 40, 31, 31, 44, 76, 41,
            32, 76, 29, 41, 38, 30, 76, 29, 41, 39, 39, 27,
            40, 30, 64, 78, 76, 76, 76, 76, 76, 76, 76, 76,
            20, 31, 38, 38, 76, 46, 34, 27, 46, 76, 35, 46,
            45, 76, 45, 29, 47, 38, 42, 46, 41, 44, 76, 49,
            31, 38, 38, 76, 46, 34, 41, 45, 31, 76, 42, 27,
            45, 45, 35, 41, 40, 45, 76, 44, 31, 27, 30, 78,
            76, 76, 76, 76, 76, 76, 76, 76, 23, 34, 35, 29,
            34, 76, 51, 31, 46, 76, 45, 47, 44, 48, 35, 48,
            31, 64, 76, 45, 46, 27, 39, 42, 31, 30, 76, 41,
            40, 76, 46, 34, 31, 45, 31, 76, 38, 35, 32, 31,
            38, 31, 45, 45, 76, 46, 34, 35, 40, 33, 45, 64,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 20, 34, 31,
            76, 34, 27, 40, 30, 76, 46, 34, 27, 46, 76, 39,
            41, 29, 37, 31, 30, 76, 46, 34, 31, 39, 64, 76,
            27, 40, 30, 76, 46, 34, 31, 76, 34, 31, 27, 44,
            46, 76, 46, 34, 27, 46, 76, 32, 31, 30, 66, 78,
            76, 76, 76, 76, 76, 76, 76, 76, 1, 40, 30, 76,
            41, 40, 76, 46, 34, 31, 76, 42, 31, 30, 31, 45,
            46, 27, 38, 64, 76, 46, 34, 31, 45, 31, 76, 49,
            41, 44, 30, 45, 76, 27, 42, 42, 31, 27, 44, 65,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 13, 51, 76,
            40, 27, 39, 31, 76, 35, 45, 76, 15, 52, 51, 39,
            27, 40, 30, 35, 27, 45, 64, 76, 11, 35, 40, 33,
            76, 41, 32, 76, 11, 35, 40, 33, 45, 66, 78, 76,
            76, 76, 76, 76, 76, 76, 76, 12, 41, 41, 37, 76,
            41, 40, 76, 39, 51, 76, 23, 41, 44, 37, 45, 64,
            76, 51, 31, 76, 13, 35, 33, 34, 46, 51, 64, 76,
            27, 40, 30, 76, 30, 31, 45, 42, 27, 35, 44, 67,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 14, 41, 46,
            34, 35, 40, 33, 76, 28, 31, 45, 35, 30, 31, 76,
            44, 31, 39, 27, 35, 40, 45, 63, 76, 18, 41, 47,
            40, 30, 76, 46, 34, 31, 76, 30, 31, 29, 27, 51,
            78, 76, 76, 76, 76, 76, 76, 76, 76, 15, 32, 76,
            46, 34, 27, 46, 76, 29, 41, 38, 41, 45, 45, 27,
            38, 76, 23, 44, 31, 29, 37, 64, 76, 28, 41, 47,
            40, 30, 38, 31, 45, 45, 76, 27, 40, 30, 76, 28,
            27, 44, 31, 78, 76, 76, 76, 76, 76, 76, 76, 76,
            20, 34, 31, 76, 38, 41, 40, 31, 76, 27, 40, 30,
            76, 38, 31, 48, 31, 38, 76, 45, 27, 40, 30, 45,
            76, 45, 46, 44, 31, 46, 29, 34, 76, 32, 27, 44,
            76, 27, 49, 27, 51, 78, 76, 76, 76, 76, 76, 76,
            76, 76]]				),
        torch.tensor([[0, 0, 0, 1069, 11]]				),
        torch.tensor([[0, 0, 0, 1069, 11]]				),
    ]
    # fmt: on
    self.assertTrue(torch.allclose(tokens[0]   ,   EXPECTED_OUTPUT[0]				)				)
    self.assertTrue(torch.allclose(tokens[1]   ,   EXPECTED_OUTPUT[1]				)				)
    self.assertTrue(torch.allclose(tokens[2]   ,   EXPECTED_OUTPUT[2]				)				)







   @require_torch
   def 						lowerCamelCase_	(     self						:						Dict				):





    """simple docstring"""


    import torch

    UpperCamelCase			  =      JukeboxTokenizer.from_pretrained("""openai/jukebox-5b-lyrics"""				)
    UpperCamelCase			  =      tokenizer(**self.metas				)["""input_ids"""]
    # fmt: off
    UpperCamelCase			  =      [
        torch.tensor([[
            0, 0, 0, 1069, 11, -1, -1, -1, -1, 9, 77, 39,
            31, 46, 77, 27, 77, 46, 44, 27, 48, 31, 38, 38,
            31, 44, 77, 32, 44, 41, 39, 77, 27, 40, 77, 27,
            40, 46, 35, 43, 47, 31, 77, 38, 27, 40, 30, 64,
            79, 77, 77, 77, 77, 77, 77, 77, 77, 23, 34, 41,
            77, 45, 27, 35, 30, 77, 72, 20, 49, 41, 77, 48,
            27, 45, 46, 77, 27, 40, 30, 77, 46, 44, 47, 40,
            37, 38, 31, 45, 45, 77, 38, 31, 33, 45, 77, 41,
            32, 77, 45, 46, 41, 40, 31, 79, 77, 77, 77, 77,
            77, 77, 77, 77, 19, 46, 27, 40, 30, 77, 35, 40,
            77, 46, 34, 31, 77, 30, 31, 45, 31, 44, 46, 63,
            77, 63, 77, 63, 77, 63, 77, 14, 31, 27, 44, 77,
            46, 34, 31, 39, 64, 77, 41, 40, 77, 46, 34, 31,
            77, 45, 27, 40, 30, 64, 79, 77, 77, 77, 77, 77,
            77, 77, 77, 8, 27, 38, 32, 77, 45, 47, 40, 37,
            77, 27, 77, 45, 34, 27, 46, 46, 31, 44, 31, 30,
            77, 48, 35, 45, 27, 33, 31, 77, 38, 35, 31, 45,
            64, 77, 49, 34, 41, 45, 31, 77, 32, 44, 41, 49,
            40, 64, 79, 77, 77, 77, 77, 77, 77, 77, 77, 1,
            40, 30, 77, 49, 44, 35, 40, 37, 38, 31, 30, 77,
            38, 35, 42, 64, 77, 27, 40, 30, 77, 45, 40, 31,
            31, 44, 77, 41, 32, 77, 29, 41, 38, 30, 77, 29,
            41, 39, 39, 27, 40, 30, 64, 79, 77, 77, 77, 77,
            77, 77, 77, 77, 20, 31, 38, 38, 77, 46, 34, 27,
            46, 77, 35, 46, 45, 77, 45, 29, 47, 38, 42, 46,
            41, 44, 77, 49, 31, 38, 38, 77, 46, 34, 41, 45,
            31, 77, 42, 27, 45, 45, 35, 41, 40, 45, 77, 44,
            31, 27, 30, 79, 77, 77, 77, 77, 77, 77, 77, 77,
            23, 34, 35, 29, 34, 77, 51, 31, 46, 77, 45, 47,
            44, 48, 35, 48, 31, 64, 77, 45, 46, 27, 39, 42,
            31, 30, 77, 41, 40, 77, 46, 34, 31, 45, 31, 77,
            38, 35, 32, 31, 38, 31, 45, 45, 77, 46, 34, 35,
            40, 33, 45, 64, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 20, 34, 31, 77, 34, 27, 40, 30, 77, 46, 34,
            27, 46, 77, 39, 41, 29, 37, 31, 30, 77, 46, 34,
            31, 39, 64, 77, 27, 40, 30, 77, 46, 34, 31, 77,
            34, 31, 27, 44, 46, 77, 46, 34, 27, 46, 77, 32,
            31, 30, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77,
            1, 40, 30, 77, 41, 40, 77, 46, 34, 31, 77, 42,
            31, 30, 31, 45, 46, 27, 38, 64, 77, 46, 34, 31,
            45, 31, 77, 49, 41, 44, 30, 45, 77, 27, 42, 42,
            31, 27, 44, 65, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 13, 51, 77, 40, 27, 39, 31, 77, 35, 45, 77,
            15, 52, 51, 39, 27, 40, 30, 35, 27, 45, 64, 77,
            11, 35, 40, 33, 77, 41, 32, 77, 11, 35, 40, 33,
            45, 66, 79, 77, 77, 77, 77, 77, 77, 77, 77, 12,
            41, 41, 37, 77, 41, 40, 77, 39, 51, 77, 23, 41,
            44, 37, 45, 64, 77, 51, 31, 77, 13, 35, 33, 34,
            46, 51, 64, 77, 27, 40, 30, 77, 30, 31, 45, 42,
            27, 35, 44, 67, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 14, 41, 46, 34, 35, 40, 33, 77, 28, 31, 45,
            35, 30, 31, 77, 44, 31, 39, 27, 35, 40, 45, 63,
            77, 18, 41, 47, 40, 30, 77, 46, 34, 31, 77, 30,
            31, 29, 27, 51, 79, 77, 77, 77, 77, 77, 77, 77,
            77, 15, 32, 77, 46, 34, 27, 46, 77, 29, 41, 38,
            41, 45, 45, 27, 38, 77, 23, 44, 31, 29, 37, 64,
            77, 28, 41, 47, 40, 30, 38, 31, 45, 45, 77, 27,
            40, 30, 77, 28, 27, 44, 31, 79, 77, 77, 77, 77,
            77, 77, 77, 77, 20, 34, 31, 77, 38, 41, 40, 31,
            77, 27, 40, 30, 77, 38, 31, 48, 31, 38, 77, 45,
            27, 40, 30, 45, 77, 45, 46, 44, 31, 46, 29, 34,
            77, 32, 27, 44, 77, 27, 49, 27, 51, 79, 77, 77,
            77, 77, 77, 77, 77, 77]]				),
        torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]				),
        torch.tensor([[0, 0, 0, 1069, 11, -1, -1, -1, -1]]				),
    ]
    # fmt: on
    self.assertTrue(torch.allclose(tokens[0]   ,   EXPECTED_OUTPUT[0]				)				)
    self.assertTrue(torch.allclose(tokens[1]   ,   EXPECTED_OUTPUT[1]				)				)
    self.assertTrue(torch.allclose(tokens[2]   ,   EXPECTED_OUTPUT[2]				)				)
